A deterministic pipeline turns any landmark source into structured scoring, feedback, and analytics - identical across every SDK.
Score every frame from 0.0 to 1.0 using joint-level tolerance rules and smoothed confidence.
Dynamic exercise tracking with phase / state-machine logic for reliable rep detection.
Convert joint deviations into actionable feedback: alignment, posture, depth, and visibility guidance.
Detect movement risks like knees past toes, rounded back, poor alignment, and bad camera position.
Render target muscle engagement and build richer workout feedback experiences.
Track tempo, asymmetry, hold duration, score history, reps, and exportable session data.
Thin platform wrappers around one shared Rust core. Same scoring, same results - idiomatic on every platform.
let session = PoseSession(config: PoseSessionConfig( exerciseJson: squatJson, exerciseType: .dynamic)) PoseSessionView(session: session) .onFrame { result in score = result.formScore }The scoring core runs in microseconds once landmarks are available, and native inference fits comfortably inside a 30fps frame on iPhone-class hardware.
Our direct native inference path across delegates - the same model running on real devices.
Where an iPhone frame goes. Compute is a sliver; the rest is headroom for capture, rendering, and your UI.
Benchmarks use static-image inference and native FFI scoring paths. Results vary by device, model, delegate, and camera pipeline.
The Rust core accepts normalized landmarks. Use a built-in provider, or plug in MediaPipe, MoveNet, Apple Vision, YOLO pose, custom ML, prerecorded video, or raw landmark streams.
Live form scoring and spoken coaching cues that adapt to every rep.
Match player movement to a reference track and score timing and accuracy.
Track range-of-motion, hold time, and symmetry for guided recovery.
Break down launch angle, tempo, and asymmetry for athletic technique.
Remote coaching and movement programs across a distributed workforce.
The short version: Pose SDK is built to sit between whatever pose detector you trust and the product experience your users actually see.
Pose SDK provides a Rust core with native SDK paths for iOS, Android, Web, React, and React Native.
Yes. The scoring core accepts normalized landmarks, so teams can bring MediaPipe, MoveNet, Apple Vision, YOLO pose models, custom ML, video replay, or raw landmark streams.
The SDK is designed for on-device processing by default. Camera frames can remain on the device while structured movement results are exposed to the app.
The SDK includes scoring logic, rep counting, exercise rules, coaching hints, safety checks, session analytics, platform wrappers, and optional UI primitives.
Yes. Teams can use the raw landmark scoring path when they already have camera, inference, or replay infrastructure and only need the movement intelligence layer.
After a technical walkthrough, evaluation access can be provisioned with guidance on the best integration path for your stack and target platforms.
Tell us what you are building. We will help you choose the right SDK path: full camera pipeline, raw landmark scoring, native mobile, web, or React Native.